235 citations · 251 across the 7 of their papers we have counts for
6 papers · 1 filter
Video Analysis and Generation via a Semantic Progress Function
Gal Metzer, Sagi Polaczek, Ali Mahdavi-Amiri +2
Transformations produced by image and video generation models often evolve in a highly non-linear manner: long stretches where the content barely changes are followed by sudden, ab…
A Neural Space-Time Representation for Text-to-Image Personalization
Yuval Alaluf, Elad Richardson, Gal Metzer +1
A key aspect of text-to-image personalization methods is the manner in which the target concept is represented within the generative process. This choice greatly affects the visual…
Set-the-Scene: Global-Local Training for Generating Controllable NeRF Scenes
Dana Cohen-Bar, Elad Richardson, Gal Metzer +2
Recent breakthroughs in text-guided image generation have led to remarkable progress in the field of 3D synthesis from text. By optimizing neural radiance fields (NeRF) directly fr…
TEXTure: Text-Guided Texturing of 3D Shapes
Elad Richardson, Gal Metzer, Yuval Alaluf +2
In this paper, we present TEXTure, a novel method for text-guided generation, editing, and transfer of textures for 3D shapes. Leveraging a pretrained depth-to-image diffusion mode…
Latent-NeRF for Shape-Guided Generation of 3D Shapes and Textures
Gal Metzer, Elad Richardson, Or Patashnik +2
Text-guided image generation has progressed rapidly in recent years, inspiring major breakthroughs in text-guided shape generation. Recently, it has been shown that using score dis…
TetGAN: A Convolutional Neural Network for Tetrahedral Mesh Generation
William Gao, April Wang, Gal Metzer +2
We present TetGAN, a convolutional neural network designed to generate tetrahedral meshes. We represent shapes using an irregular tetrahedral grid which encodes an occupancy and di…